Image contrast enhancement

ABSTRACT

A method for contrast enhancement for digital images, including filtering an original image having original color values, to generate a first filtered image corresponding to bright color values, and a second filtered image corresponding to dark color values, deriving local highlight multipliers by applying a highlight response curve to the first filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, deriving local shadow multipliers by applying a shadow response curve to the second filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, deriving local offset values by applying an offset curve to the first filtered image, and processing the original image, including subtracting the local offset values from the original color values to generate shifted color values, multiplying the shifted color values by the local highlight multipliers, and further multiplying the shifted color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image. A system and a computer-readable storage medium are also described.

COMPUTER PROGRAM LISTING

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent & Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates to the field of digital image processing, and more particularly to enhancement of digital images.

BACKGROUND OF THE INVENTION

Digital image enhancement concerns applying operations to a digital image in order to improve a picture, or restore an original that has been degraded. Digital image enhancement dates back to the origins of digital image processing. Unlike film-based images, digital images can be edited interactively at will, and detail that is indiscernible in an original image can be brought out. Often images that appear too dark because of poor illumination or too bright because of over illumination can be restored to accurately reflect the original scene.

Enhancement techniques involve digital image processing and can include inter alia filtering, morphology, composition of layers and random variables. Conventional image processing software often includes a wide variety of familiar editing tools used for enhancing an image, such as edge sharpening, smoothing, blurring, histogram equalization, gamma correction, dithering, color palate selection, paint brush effects and texture rendering. Generally, enhancement techniques include one or more parameters, which a user can dynamically adjust to fine-tune the enhanced image.

Several factors distinguish one enhancement technique over another, including inter alia (i) visual quality of the enhanced image, (ii) robustness, (iii) speed and (iv) ease of use. Requirements (ii) and (iv) generally conflict, since the ability to enhance a variety of different types of imagery generally requires a large number of complex adjustable parameters, where ease of use requires a small number of simple adjustable parameters, each of which is intuitive in its effect on the enhanced image. Regarding requirement (iii), for interactive image editing applications, wherein a user repeatedly adjusts enhancement parameters and watches as the enhanced image changes, if the enhancement operation takes more than a fraction of a second to complete, the performance becomes sluggish, and the capability of accurately fine-tuning the enhancement as desired deteriorates. This speed requirement, together with the proliferation of mega-pixel color images, makes design of an enhancement algorithm quite challenging.

Contrast enhancement is a particular aspect of image enhancement that concerns color variations that are not clearly discernible within an image, because of dark shadows or bright highlights. Because of the eye's relative insensitivity to variations in dark colors and variations in bright colors, important details within an image can be missed. Conventional contrast enhancement can involve transfer functions for expanding dynamic range in some parts of the color spectrum and compressing dynamic range in other parts, and gamma correction for adjusting brightness and contrast.

U.S. Pat. Nos. 6,633,684 and 6,677,959 to James describe contrast enhancement by fitting an original source image, such as an x-ray, between upper and lower low frequency images, and expanding the dynamic color range locally to an interval of color values between the lower and upper frequency image color values. Such expansion serves to visually amplify subtle variations in color value between the lower and upper frequency image color values.

SUMMARY OF THE DESCRIPTION

The present invention concerns a novel method and system for image enhancement that uses one or more filtered images to generate offsets and multipliers for adjusting pixel color values. The image enhancement can be controlled through adjustable user parameters that perform leveling and contrast adjustment for both shadow portions and highlight portions of an original image. These parameters are intuitive and easy to adjust.

The present invention can be implemented extremely efficiently, so as to achieve real-time performance whereby image enhancement is performed within a fraction of a second immediately upon adjustment of a user parameter, for multi-mega-pixel images captured by today's digital cameras. As such, a user is provided with continuous real-time feedback, which enables extremely accurate fine-tuning of the enhancement. Using the present invention, a user is able to instantly compare an enhanced image with a source image, and compare one enhanced image with another enhanced image.

Experimental results have shown that the present invention is robust, and yields excellent quality results, even for images where most of the detail is obscured in shadows or bright highlights.

Specifically, the present invention generates two response curves, one for shadows and one for highlights, each a function of color value. The values of the response curves are used as multipliers for pixel color values. The response curve for highlights is an exponential curve, and increases monotonically from a value of one, corresponding to a color value of 0, to a value greater than or equal to one, corresponding to a maximum color value. As such, the multiplier for highlights visually amplifies bright color variation. The response curve for shadows is also an exponential curve, and decreases monotonically from a value greater than or equal to one, corresponding to a color value of 0, to a value of one, corresponding to a maximum color value. As such, the multiplier for shadows visually amplifies dark color variation.

In accordance with a preferred embodiment of the present invention, the highlight response curve is derived from a first filtered image and the shadow response curve is derived from a second filtered image. In one embodiment of the present invention, the first filtered image is a filter of an image of minimum source color values and the second filtered image is a filter of an image of maximum source color values. In another embodiment of the present invention, the first and second filtered images are filters of an image of source luminance values.

In addition, the present invention uses an offset curve, which is also a function of color value, to scale dynamic color range to the range of color values greater than or equal to the offset value.

Although U.S. Pat. Nos. 6,633,684 and 6,677,959 to James describe use of upper and lower low frequency images, in distinction to James the present invention generates response curves for use as multipliers, whereas James users the upper and lower low frequency images for local scaling of dynamic range.

There is thus provided in accordance with a preferred embodiment of the present invention a method for contrast enhancement for digital images, including filtering an original image having original color values, to generate a first filtered image, corresponding to bright color values, and a second filtered image corresponding to dark color values, deriving local highlight multipliers by applying a highlight response curve to the first filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, deriving local shadow multipliers by applying a shadow response curve to the second filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, deriving local offset values by applying an offset curve to the first filtered image, and processing the original image, including subtracting the local offset values from the original color values to generate shifted color values, multiplying the shifted color values by the local highlight multipliers, and further multiplying the shifted color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

There is further provided in accordance with a preferred embodiment of the present invention a system for enhancing contrast of digital images, including a filter processor for filtering an original image having original color values, to generate a first filtered image, corresponding to bright color values, and a second filtered image corresponding to dark color values, and an image enhancer coupled to said filter processor for (i) deriving local highlight multipliers by applying a highlight response curve to the first filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, (ii) deriving local shadow multipliers by applying a shadow response curve to the second filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, (iii) deriving local offset values by applying an offset curve to the first filtered image, (iv) subtracting the local offset values from the original color values to generate shifted color values, (v) multiplying the local shifted color values by the highlight multipliers, and (vi) further multiplying the shifted color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

There is yet further provided in accordance with a preferred embodiment of the present invention a computer-readable storage medium storing program code for causing a computer to perform the steps of filtering an original image having original color values, to generate a first filtered image, corresponding to bright color values, and a second filtered image corresponding to dark color values, deriving local highlight multipliers by applying a highlight response curve to the first filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, deriving local shadow multipliers by applying a shadow response curve to the second filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, deriving local offset values by applying an offset curve to the first filtered image, and processing the original image, including subtracting the local offset values from the original color values to generate shifted color values, multiplying the shifted color values by the local highlight multipliers, and further multiplying the shifted color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

There is additionally provided in accordance with a preferred embodiment of the present invention a method for contrast enhancement for digital images, including filtering an original image having original color values, to generate a filtered image corresponding to bright color values, deriving local highlight multipliers by applying a highlight response curve to the filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, deriving local offset values by applying an offset curve to the filtered image, and processing the original image, including subtracting the local offset values from the original color values to generate shifted color values, and multiplying the shifted color values by the local highlight multipliers.

There is moreover provided in accordance with a preferred embodiment of the present invention a method for contrast enhancement for digital images, including filtering an original image having original color values, to generate a filtered image corresponding to dark color values, deriving local shadow multipliers by applying a shadow response curve to the filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, and processing the original image, comprising multiplying the original color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

There is further provided in accordance with a preferred embodiment of the present invention a system for enhancing contrast of digital images, including a filter processor for filtering an original image having original color values, to generate a filtered image corresponding to bright color values, and an image enhancer coupled to said filter processor for (i) deriving local highlight multipliers by applying a highlight response curve to the filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, (ii) deriving local offset values by applying an offset curve to the filtered image, (iii) subtracting the local offset values from the original color values to generate shifted color values, and (iv) multiplying the shifted color values by the local highlight multipliers, thereby generating a contrast-enhanced image from the original image.

There is yet further provided in accordance with a preferred embodiment of the present invention a system for enhancing contrast of digital images, including a filter processor for filtering an original image having original color values, to generate a filtered image corresponding to dark color values, and an image enhancer coupled to said filter processor for (i) deriving local shadow multipliers by applying a shadow response curve to the filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, and (ii) multiplying the original color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

There is additionally provided in accordance with a preferred embodiment of the present invention a computer-readable storage medium storing program code for causing a computer to perform the steps of filtering an original image having original color values, to generate a filtered image corresponding to bright color values, deriving local highlight multipliers by applying a highlight response curve to the filtered image, the highlight response curve being a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, deriving local offset values by applying an offset curve to the filtered image, and processing the original image, including subtracting the local offset values from the original color values to generate shifted color values, and multiplying the shifted color values by the local highlight multipliers.

There is moreover provided in accordance with a preferred embodiment of the present invention a computer-readable storage medium storing program code for causing a computer to perform the steps of filtering an original image having original color values, to generate a filtered image corresponding to dark color values, deriving local shadow multipliers by applying a shadow response curve to the filtered image, the shadow response curve being a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value, and processing the original image, comprising multiplying the original color values by the local shadow multipliers, thereby generating a contrast-enhanced image from the original image.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more fully understood and appreciated from the following detailed description, taken in conjunction with the drawings in which:

FIG. 1A is an illustration of filter windows and sub-filter windows used in deriving a median filtered image, in accordance with a preferred embodiment of the present invention;

FIG. 1B is an illustration of filter windows and sub-filter windows used in deriving a weighted average filtered image, in accordance with a preferred embodiment of the present invention;

FIG. 2 is a simplified block diagram of the essential components of a system for interactively enhancing digital images, in accordance with a preferred embodiment of the present invention; and

FIG. 3 is a simplified flowchart of the essential steps for interactively enhancing digital images, in accordance with a preferred embodiment of the present invention;

FIGS. 4A-4F illustrate an image before and after enhancement, an image of overexposed pixel locations for the enhanced image, and various filtered images used in the enhancement process, in accordance with a preferred embodiment of the present invention; and

FIG. 5 is a flow diagram of the principal software methods in the source code listed in Appendices A and B, in accordance with a preferred embodiment of the present invention.

LIST OF APPENDICES

Appendix A is a detailed listing of computer source code written in the C++ programming language for implementing a median or a weighted average filter in accordance with a preferred embodiment of the present invention; and

Appendix B is a detailed listing of computer source code written in the C++ programming language for implementing for implementing color enhancement, in accordance with a preferred embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is preferably embodied on a general-purpose consumer-grade computer, including a processor for executing an image-processing software application, a memory for storing program code and for storing one or more digital images, one or more input devices such as a keyboard and a mouse for enabling a user to interact with the software application, a display device, and a bus for intercommunication between these components. Preferably, the computer also includes an interface for receiving digital images from a scanner and from a digital camera, and a network connection for receiving and transmitting digital images over the Internet. The present invention can be embodied in mobile computing devices, in Internet appliances such as electronic picture frames, within vehicles such as airplanes, and within equipment such as medical scanners. The present invention can be implemented in software or in general-purpose or special-purpose hardware, or in a software-hardware combination.

The present invention operates by processing an original source image and deriving a contrast-enhanced image therefrom. The present invention applies to grayscale and color images, with arbitrary color depth. In a preferred embodiment, the present invention includes two phases; namely, a first phase (“Phase One”) for deriving one or more filtered images, and a second phase (“Phase Two”) for using the one or more filtered images to derive local highlight and shadow multipliers, and local offset values, for each pixel location of the original source image. The enhancement process preferably subtracts the local offset values from color values of the original source image, and multiplies the resulting differences by the local highlight and shadow multipliers.

The highlight and shadow multipliers are preferably derived from highlight and shadow response curves that visually amplify color variation in bright and dark portions of the source image, respectively. The offset values are preferably derived from curves that stretch the contrast of pixels away from the maximum color value.

Preferably, the highlight response curve is an exponential function of color value, of the general form f(x)=1+k*x^(n), increasing monotonically from a value of one, corresponding to a color value of zero, which is the darkest color value, to a value greater than or equal to one, corresponding to a maximum color value, which is the brightest color value. The variable x is a linearly scaled color value, which ranges from x=0 to x=1 as the color value ranges from zero to its maximum value. Similarly, the shadow response curve is preferably an exponential function of color value, of the general form f(x)=1+k*(1−x)^(n), decreasing monotonically from a value greater than or equal to one, corresponding to a color value of zero, to a value of one, corresponding to the maximum color value. As such, the highlight response curve, when used as a multiplier, serves to visually amplify color variations for bright colors; and the shadow response curve, when used as a multiplier, serves to visually amplify color valuations for dark colors. Thus careful construction of the highlight and shadow response curves results in high quality contrast enhancement.

In accordance with a preferred embodiment of the present invention, the highlight and shadow response curves are controllable by adjusting values of parameters. These parameters determine the shapes of the highlight and shadow response curves. A user can fine-tune values of these parameters interactively, using a graphical user interface, in order to obtain a satisfactory contrast-enhanced image.

Further in accordance with a preferred embodiment of the present invention, the highlight and shadow multipliers are not obtained by applying the highlight and shadow response curves directly to the source image, respectively, since the benefit of the contrast enhancement to bring out detail in the highlight and shadow areas, would be offset by detailed variation in the multipliers at each pixel location. In order for contrast enhancement to be effective, the highlight and shadow multipliers should be relatively insensitive to local detail variation in the source image.

Instead, the highlight and shadow multipliers are preferably derived by applying the highlight and shadow response curves to corresponding filtered versions of the source image. The filtered versions serve to dampen local detail variation, and thus provide relatively smooth local color values to which the response curves can be applied, and thus a smooth base upon which to derive local highlight and shadow multipliers for the source image.

The present invention can be embodied using a variety of types of filters, such as filters based on medians and filters based on weighted averages, which have been found to be very suitable. Preferably, filter parameters such as window sizes and choice of weights can be adjusted by a user.

Enhancement Algorithm

For purposes of organization, a preferred enhancement algorithm is described in the following description according to Phase One and Phase Two, according to grayscale image enhancement and color image enhancement, and according to type of filters used. Specifically, Phase One is described hereinbelow for two preferred types of filters; a modified median filter, and a modified weighted average filter.

The modified filters used in the present invention differ from the prior art in that sub-window averages of pixel color values are used in place of single pixel color values. I.e., entire sub-windows of pixel values are treated as if they are lumped together and located at single pixel locations. Such modification is better suited for large windows, say, with dimensions on the order of 100 pixels, and yield better representations of local contrast than prior art filters.

1. Phase One

Phase One is also described hereinbelow for grayscale images, which have a single color channel, and color images, which have multiple color channels. Two approaches are described for color image enhancement, a first approach that uses a single filtered image, based on filtering luminance source color values; and a second approach that uses two filtered images, based on filtering minimum and maximum source color values.

1.1 Modified Median Filter

In this embodiment, a modified median filter is applied to a source image, I_(source), to derive a filtered image I_(filter). The median filter used in the preferred embodiment is a hybrid multi-stage median filter, which is a modified version of the finite-impulse response (FIR) median hybrid filter described in Nieminen, A., Heinonen, P. and Neuvo, Y., “A new class of detail-preserving filters for image processing,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 9, January 1987, and in Arce, G., “Detail-preserving ranked-order based filters for image processing,” IEEE Trans. Acoustics, Speech and Signal Processing, Vol. 37, No. 1, January 1989. In distinction to the prior art, the present invention uses squares instead of directed lines to construct the sub-filter windows.

An advantage of using squares with median hybrid filters instead of directed lines is that the number of directed lines required to preserve details is large, when using large filter window sizes. The prior art uses 8 sub-filters with a filter of radius 2, and more sub-filters, which use directed lines at additional angles, are generally recommended with filters of larger radii. For the present invention, a filter radius as large as 500 is not uncommon, and thus the prior art would require an enormous number of sub-filters.

Another advantage of using squares with median hybrid filters instead of directed lines, is that the average value computed at each square may be re-used eight times over the course of deriving the median filtered image of the present invention.

Reference is now made to FIG. 1, which is an illustration of filter windows and sub-windows used in deriving a modified median filtered image, in accordance with a preferred embodiment of the present invention. Shown in FIG. 1 is a filter window of dimension (2N+1)×(2N+1) pixels, centered at a pixel (i,j), together with eight sub-windows, each of dimension (2M+1)×(2M+1) pixels. The eight sub-windows are designated “East,” “West,” “North,” “South,” “Northwest,” “Southeast,” “Northeast” and “Southwest,” according to their relative positions with respect to pixel (i,j). Thus the center of the West window, for example, is (i−(N−M),j), and its pixel coordinates range from (i−N,j−M) at the lower left corner to (i−N+2M,j+M) at the upper right corner. Here N and M are arbitrary numbers with 0≦M≦N, and the pixel numbering advances from left to right (first coordinate), bottom to top (second coordinate). In the illustration shown in FIG. 1, N=10 and M=3.

In accordance with a preferred embodiment of the present invention, given a source image, I_(source), for each pixel location (i, j), the averages of I_(source) over each of the sub-windows are computed. The average of I_(source) over the West sub-window; namely,

$\begin{matrix} {{\frac{1}{\left( {{2M} + 1} \right)^{2}}{\sum\limits_{i = {- M}}^{M}{\sum\limits_{j = {- M}}^{M}{I_{source}\left( {{i - N + M},j} \right)}}}},} & (1) \end{matrix}$ is denoted I-West(i,j), the (i,j) denoting that the West sub-window is positioned westward relative to pixel location (i,j). The other averages are similarly denoted.

When the various averages have been computed, the following seven medians are determined: med-1(i,j)=median(I-East(i,j),I-West(i,j),I _(source)(i,j));  (2a) med-2(i,j)=median(I-North(i,j),I-South(i,j),I _(source)(i,j));  (2b) med-3(i,j)=median(I-Northwest(i,j),I-Southeast(i,j),I _(source)(i,j));  (2c) med-4(i,j)=median(I-Northeast(i,j),I-Southwest(i,j),I _(source)(i,j));  (2d) med-12(i,j)=median(med-1(i,j),med-2(i,j),I _(source)(i,j));  (2e) med-34(i,j)=median(med-3(i,j),med-4(i,j),I _(source)(i,j); and  (2f) I _(filter)(i,j)=median(med-12(i,j),med-34(i,j),I _(source)(i,j)).  (2g) The median of three numbers is the number in the middle when the three numbers are ordered.

The result of the seventh median calculation in Equation (2g) is the preferred median filter. A way to represent this median filter is to imagine each sub-filter window lumped into a single pixel positioned adjacent to pixel (i, j) according to the relative position of the sub-window, with a pixel value equal to the average value of the source image over the sub-window. This determines a 3×3 pixel window,

a b c d x e f g h centered at pixel location (i,j), where x=I_(source)(i,j), a=I-Northwest(i,j) and similarly for b through h. Then the value of I_(filter)(i,j) is determined by u=median(median(d,e,x),median(b,g,x),x);  (3a) v=median(median(a,h,x),median(c,f,x),x); and  (3b) I _(filter)(i,j)=median(u,v,x).  (3c) 1.1.1 Modified Median Filter—Grayscale Source Image

When the original source image is a grayscale image, with a single color component, then equations (2a)-(2g) directly determine the preferred median filter I_(filter).

1.1.2 Modified Median Filter with Filtered Luminance Image—Color Source Image

For an original source image that is a color image with red, green and blue pixel color components, (R(i,j), G(i,j), B(i,j)), two preferred methods for determining median filtered images are described herein. A first preferred method uses the luminance component of the pixel color values, L_(source), as a source image, and derives a median filtered image, L_(filter), therefrom. The luminance component of a pixel color with color values (R, G, B) is preferably determined by a conventional formula such as L=0.299*R+0.587*G+0.114*B.

1.1.3 Modified Median Filter with Filtered Minimum and Maximum Images—Color Source Image

A second preferred method uses two median filtered images, based on the maximum and minimum of the RGB color values. Specifically, denote MIN_(source)(i,j)=minimum(R(i,j), G(i,j), B(i,j)) and MAX_(source)(i/i)=maximum(R(i,j), G(i,j), B(i,j)). The median filtered image from MIN_(source), denoted MIN_(filter), and the median filtered image from MAX_(source), denoted MAX_(filter), are both used in the second preferred method.

1.2 Modified Weighted Average Filter

It may be appreciated by those skilled in the art that filters other than median filters may be used in Phase One of the present invention. It has been found by experimentation with various types of filters that certain weighted average filters perform well with contrast enhancement, in particular when the filter coefficients generally have an inverse gradient dependency, as described hereinbelow.

As with the modified median filter, the modified weighted average filter operates on an original source image, I_(source), to produce a filtered image, I_(filter). The modified weighted filter is computed using weighted averages of sample values, where the weights corresponding to sample values are constructed as described hereinbelow.

In accordance with a preferred embodiment of the present invention, within a sliding square filter window of width and height 2N+1 centered at pixel location (i,j), square sub-windows, each of width and height 2M+1 are positioned so as to be centered at pixel locations (i+kd, j+ld), for some fixed integer parameter d. It is assumed that M is a divisor of N, i.e., N=Mb, for some integer b; and that the parameter d, which controls the spacing between the sub-windows, is a divisor of N−M, i.e., N−M=nd, for some integer n. The indices k and l above independently range from −n to +n. The average color value over such sub-window, uniformly averaged over the (2M+1)² pixels within the sub-window, is denoted by a_(kl) and is considered as if it is lumped into a single pixel value.

The filtered value I_(filter)(i,j) is computed as a weighted average of the color value at (i,j), together with the (2n+1)² sub-window averages a_(kl). For example, with n=2 there are twenty-five sub-window averages, say a₋₂₋₂, a₋₂₋₁, . . . , as follows:

a⁻²² a⁻¹² a₀₂ a₁₂ a₂₂ a⁻²¹ a⁻¹¹ a₀₁ a₁₁ a₂₁ a⁻²⁰ a⁻¹⁰ a₀₀ a₁₀ a₂₀ a⁻²⁻¹ a⁻¹⁻¹ a⁰⁻¹ a¹⁻¹ a²⁻¹ a⁻²⁻² a⁻¹⁻² a⁰⁻² a¹⁻² a²⁻² Each of the values a_(kl) denotes the average of the pixel color values of I_(source) over a sub-window of size (2M+1)×(2M+1), centered at (i+kd, j+ld). For example, the sub-window corresponding to a₋₂₂ has its lower left corner at pixel location (i−2d−M, j+2d−M) and its upper right corner at pixel location (i−2d+M, j+2d+M).

Reference is now made to FIG. 1B, which is an illustration of filter windows and sub-windows used in deriving a modified weighted average filtered image, in accordance with a preferred embodiment of the present invention. Shown in FIG. 1B is a (2N+1)×(2N+1) filter window centered at pixel location (i,j). Also shown are pixel locations with spacings of d between them, vertically and horizontally, centered at pixel location (i,j). The pixel with a solid black fill color, for example, is located at (i+2d, j−d). The sub-window illustrated in FIG. 1B of dimensions (2M+1)×(2M+1), is centered at the solid black pixel. Such sub-windows are positioned at each of the twenty-five pixel locations (i+kd, j+ld) as k and l each range independently from −2 to +2, and the average of the (2M+1)² color values within each such sub-window is the quantity denoted by a_(kl) hereinabove. The average over the sub-window illustrated in FIG. 1B, for example, is the quantity a²⁻¹.

It may be appreciated by those skilled in the art that the sub-window averages a_(kl) may be simple uniform averages, as described above, or non-uniform averages, such as Gaussian blur averages.

The filtered value for pixel location (i,j) is given by a weighted average

$\begin{matrix} {I_{filter} = {{wI}_{source} + {\sum\limits_{k = n}^{n}{\sum\limits_{l = {- n}}^{n}{w_{kl}a_{kl}}}}}} & (4) \end{matrix}$ where the weights w and w_(kl) are normalized so as to sum to one. In applying Equation (4), the terms I_(filter), I_(source) and the terms a_(kl) in Equation (4) are evaluated at specific pixel locations (i,j); i.e., I_(filter)(i,j), I_(source)(i,j) and a_(kl)(i,j).

It may be appreciated by those skilled in the art that the choice of b and n can be used to provide a trade-off between computational complexity and accuracy in the filtered image. Increasing b and n yields a filtered image with fewer artifacts due to the small sub-window sizes, but increases the computational time. In the limiting case where M=0 and d=1, the modified weighted average in Equation (4) reduces to a conventional weighted average filter.

Various choices for the weights correspond to different types of filters. It is expected that good choices place more weight on sample values that have a value closer to the value of the center pixel. For inverse gradient filters, for example, the weights are chosen so that less weight is placed on averages a_(kl) that differ more from the value of I_(source)(i,j). Four candidate methods for assigning the weights are as follows:

-   -   1. Assign w=1, and w_(kl)=1 or 0 according to whether Delta is         less than Sigma or not, respectively, where         Delta=|I_(source)−a_(kl)|, and Sigma is a constant.     -   2. Assign w=1, and w_(kl)=(1+Delta/k1)^(−k2), where k1 and k2         are constants.     -   3. Assign w=1, and w_(kl)=exp(−Delta²/Sigma²).     -   4. Assign w=1, and w_(kl)=exp(−r²/k²−Delta²/Sigma²), where r is         the distance between the center pixel (i,j) and the center of         the sub-window corresponding to a_(kl), and k is a constant. For         computational purposes, r is calculated as the Euclidean pixel         distance normalized by the pixel distance between adjacent         samples; i.e., r=sqrt[(i−k)²+(j−l)²]/d. Such normalization by d         ensures that the weight coefficients are independent of N.         In each method, 1-4, the weights are preferably re-normalized         after being determined as above, by dividing them by their sum,         thus ensuring that the normalized weights add up to one. The         inverse gradient feature requires that the weights w_(kl) be         decreasing or, more precisely, non-increasing, in their         dependence on Delta. With method 4 above, the weights are also         decreasing in their dependence on the distance, r, of the         sub-window a_(kl) from the center pixel (i,j).

Respective references for the weightings in methods 1-4 are:

-   1. Lee, J.-S., “Digital image smoothing and the Sigma Filter,”     Computer Vision, Graphics and Image Processing, Vol. 24, 1983, pages     255-269. -   2. Wang, D. C. C., Vagnucci, A. H. and Li, C. C., “Gradient inverse     weighted smoothing scheme and the evaluation of its performance,”     Computer Graphics and Image Processing, Vol. 15, 1981, pages     167-181. -   3. Saint-Marc, P., Chen, J. S. and Medioni, G., “Adaptive smoothing:     a general tool for early vision,” Proceedings of the Conference on     Computer Vision and Pattern Recognition, 1989, pages 618-624. -   4. Smith, S. and Brady, J., “SUSAN—a new approach to low level image     processing,” International Journal of Computer Vision, Vol. 23, No.     1, 1997, pages 45-78.     In distinction from the present invention, reference 2 teaches a     different expression for Delta, and does not use the exponent k2 nor     the addition of 1 to Delta/k1. Reference 3 teaches a discrete     gradient, as computed within a 3×3 window, instead of     Delta=|I_(source)−a_(kl)|, as above. Additionally, reference 3     teaches a term 2k² in the denominator of the exponential term,     instead of Sigma², as above. Reference 4 above teaches a value of     w=0 for the weight at the center pixel (i,j). However, it has been     found that, for the present invention, a value of w=1 yields better     performance for contrast enhancement.

A particular characteristic of weighted average filters, as opposed to median filters, is that weighted average filters, in general, are primarily sensitive to magnitude of detail in addition to size. Median filters, on the other hand in general, are primarily sensitive only to size. Thus using a weighted average filter, small but very bright, or very dark, features of an image are left intact in the filtered image—which serves to protect such areas from being over-exposed or under-exposed in Phase Two. As such, whereas the parameter N often has to be reduced using a median filter in order to prevent over-exposure or under-exposure in Phase Two, this is generally not required when using a weighted average filter. In fact, using a large value of N tends to be beneficial with weighted average filters, for preserving relative balance of objects in an image. For example, a small value of N tends to eliminate shadows from a face, giving the face an unnatural flat look, whereas a large vale of N preserves the shadows.

2. Phase Two

Phase Two of a preferred of the present invention derives a desired enhanced image, denoted I_(enhanced), from the source image I_(source) and from the filtered images computed in Phase One. Various parameters used in the derivation of I_(enhanced) are user-adjustable, as described hereinbelow. As such, the user can refine the enhanced image by iteratively adjusting these user parameters based on the appearance of the enhanced image.

2. Grayscale Images

For grayscale images, Phase Two proceeds as follows:

$\begin{matrix} {{I_{enhanced} = {\left( {I_{source} - g_{offset}} \right)*g_{\min}*g_{\max}}},{where}} & \left( {5a} \right) \\ {{g_{\min} = {1 + {k_{hl}\left( \frac{I_{filter}}{S} \right)}^{k_{hc}}}},} & \left( {5b} \right) \\ {{g_{\max} = {1 + {k_{sl}\left( {1 - \frac{I_{filter}}{S}} \right)}^{k_{sc}}}},} & \left( {5c} \right) \\ {{g_{offset} = {S*\left( {1 - {1/g_{\min}}} \right)}},} & \left( {5d} \right) \end{matrix}$ S is the maximum value for a color component (e.g., 255 for 8-bit color channels), and k_(hl), k_(hc), k_(sl) and k_(sc) are user-adjustable parameters. Specifically,

k_(hc) is the “highlight contrast” and ranges between 1 and 25;

k_(hl) is the “highlight level” and ranges between 0 and a maximum value determined from k_(hc), as described hereinbelow;

k_(sc) is the “shadows contrast” and ranges between 1 and 25; and

k_(sl) is the “shadows level” and ranges between 0 and a maximum value determined from k_(sc), as described hereinbelow.

Preferably, I_(enhanced) is clipped to take values between 0 and S; i.e., I_(enhanced) is set to zero if the value calculated in Equation (5a) is negative, and I_(enhanced) is set to S if the value calculated in Equation (5a) exceeds S.

The choice of g_(offset) is such that color value I=S is a fixed-point of the function (I−g_(offset))*g_(min). Since g_(min) is generally greater than one, this function serves to stretch color values, I, away from S.

In accordance with a preferred embodiment of the present invention, Equations (5a)-(5d) are applied point-wise at each pixel location (i,j) within the enhanced image. I.e., I_(enhanced) in Equation (5a) corresponds to I_(enhanced)(i,j), I_(source) corresponds to I_(source)(i,j) and I_(filter) corresponds to I_(filter)(i,j).

2.2 Color Images

For color images with red, green and blue color components, Phase Two proceeds as follows: C _(enhanced)=(1−k _(CB))*(E−D)+[(1−k _(CB))*D+k _(CB) *E]*g _(min) *g _(max)  (6a) where the terms D and E are given by

$\begin{matrix} {{D = {L_{source} - g_{offset}}},} & \left( {6b} \right) \\ {E = {C_{source} - {\left\{ {\left\lbrack {{k_{CB}*L_{source}} + {\left( {1 - k_{CB}} \right)*C_{source}}} \right\rbrack/L_{source}} \right\}*g_{offset}}}} & \left( {6c} \right) \\ {{g_{\min} = {1 + {k_{hl}\left( \frac{{MIN}_{filter}}{S} \right)}^{k_{hc}}}},} & \left( {6d} \right) \\ {{g_{\max} = {1 + {k_{sl}\left( {1 - \frac{{MAX}_{filter}}{S}} \right)}^{k_{sc}}}},} & \left( {6e} \right) \end{matrix}$ and where

C_(source) is a color component from the source image;

L_(source) is a luminance component from the source image;

C_(enhanced) is the enhanced color component;

k_(CB) is a user-adjustable parameter—the “color boost” parameter, ranging between 0 and 1; and

the parameters g_(offset), S, k_(hl), k_(hc), k_(sl) and k_(sc) are as defined hereinabove with respect to Equations (5a)-(5d).

I.e., Equation (6a) represents three equations, for C corresponding to (i) the red color component, (ii) the green color component and (iii) the blue color component.

It is noted that for the special case where C_(source)=L_(source), or for the special case where k_(CB)=1, then Equation (6a) reduces to C _(enhanced)=(C _(source) −g _(offset))*g _(min) *g _(max), consistent with Equation (5a) hereinabove. As such, the color boost parameter k_(CB) can be considered as a weighting, or mixing factor between C_(source) and L_(source), with no weight on L_(source) when k_(CB)=1. It is further noted that for the special case where k_(CB)=0, then Equation (6a) reduces to C _(enhanced)=(L _(source) −g _(offset))*(g _(min) *g _(max) +C _(source) /L _(source)−1). For example, if g_(min)=1 and g_(max)=2, then g_(offset)=0 and the above expression reduces to C_(enhanced)=C_(source)+L_(source).

Equations (6a)-(6e) correspond to the embodiment of Phase One for color images described hereinabove in Section 1.1.3, in which minimum and maximum images are filtered. In the embodiment of Phase One for color images described hereinabove in Section 1.1.2, in which the luminance image is filtered, both MIN_(filter) and MAX_(filter) are replaced by L_(filter) in Equations (6d) and (6e).

As described hereinabove with respect to Equations (5a)-(5d), the enhanced color components C_(enhanced) are preferably clipped so as to range from 0 to S.

It is noted that for color images, Equations (5a)-(5d) are not simply applied independently to each individual color component, since doing so would yield an over-saturated image as a result of the multiplications by g_(min) and g_(max), each of which is greater than one. Instead, Equations (6a)-(6e) include multiplications by g_(min) and g_(max), which serve to increase saturation; and addition of terms (1−k_(CB))*D and k_(CB)*E, which serve to reduce saturation.

Implementation Details

In a preferred embodiment of Phase One of the present invention, the various sub-window averages are computed once, and re-used eight times for the modified media filter, and re-used (2n+1)² times for the weighted average filter. For example, the East average relative to pixel location (i,j) is identical to the West average relative to pixel (i+2*(N−M), j) and to the North average relative to pixel (i+(N−M), j−(N−m)) and to the South average relative to pixel (i+(N−M), j+(N−M)), etc., as can be seen from FIG. 1. Thus it may be appreciated by those skilled in the art that the various sub-window averages may be stored in a sliding window and re-used.

It may further be appreciated that since a square neighborhood average is a separable convolution filter, the two-dimensional summation in Equation (1) reduces to two summations, each over 2M+1 pixel locations; namely, one sum in the vertical direction and the other sum in the horizontal direction. Moreover, a one-dimensional sliding window average is preferably computed by a moving sum. As the window slides one pixel to the right, the new pixel within the window is added into the sum, and the pixel that “slid out of” the window is subtracted.

As a result, the computation of each of the medians in Equations (2a)-(2d) is achieved using only two additions, two subtractions and one division per pixel. The division can be replaced with a look-up table of size S*(2M+1)², where S is a maximum color value. For 8-bit color channels, S=255.

Reference is now made to Appendix A, which is a detailed listing of computer source code written in the C++ programming language for implementing a median or a weighted average filter in accordance with a preferred embodiment of the present invention. It is noted that the listing Appendix A includes an integer-arithmetic implementation, for faster performance.

In a preferred embodiment of Phase Two of the present invention, computation of MIN_(filter) and MAX_(filter) is also performed using a sliding window. It may thus be appreciated that, using the present invention, it is not necessary to store intermediate results in an intermediate buffer of the same dimensions as the source image.

For images having color channels with more than eight bits per color, the present invention preferably down-samples the images to eight bits per color prior to application of the filter. Since, as per Equation (6a) above, the original image is processed at its full dynamic range in Phase Two, the down-sampling of Phase One results in no loss of detail.

Reference is now made to Appendix B, which is a detailed listing of computer source code written in the C++ programming language for implementing for implementing color enhancement, in accordance with a preferred embodiment of the present invention. For computational efficiency, the terms g_(min) and g_(max) are preferably pre-computed for all values of MIN and MAX, and the results are stored in look-up tables. This eliminates the need for re-computing the exponential terms in Equations (6d) and (6e) as the enhanced image pixel color values are being determined.

It is further noted that the parameters k_(hl), k_(hc), k_(sl), k_(sc) and k_(CB) do not impact the filter calculations. As such, the currently saved filtered images can be re-used when a user adjusts these parameters. This eliminates the need for re-applying the filter.

Reference is now made to FIG. 2, which is a simplified block diagram of the essential components of a system for interactively enhancing digital images, in accordance with a preferred embodiment of the present invention. As shown in FIG. 2, a source image is processed by a filter processor 210, to derive one or more filtered images. Full details of operation of filter processor 210, in accordance with a preferred embodiment of the present invention, are provided in the computer source code listing in Appendix A.

As described hereinabove, if the source image is a grayscale image, I_(source), then filter processor 210 preferably derives a filtered image, I_(filter). If the source image is a color image, then filter processor 210 preferably derives, either (i) a filtered luminance image, L_(filter), in accordance with a first preferred embodiment of the present invention, or (ii) two filtered images, MIN_(filter) and MAX_(filter), in accordance with a second preferred embodiment. The selection of first or second preferred embodiment is set by a user-adjustable parameter, METHOD.

Filter processor 210 uses window size parameters, M and N, as described hereinabove, which are preferably adjustable by a user.

An image enhancer 220 derives an enhanced image, based on the source image and the one or more filtered images derived by filter processor 210. Image enhancer 220 includes a module 240 for applying an offset curve and highlight and shadow response curves to the filtered images, to obtain offset values and highlight and shadow multipliers, respectively. Specifically, module 240 computes the terms g_(min), g_(max) and g_(offset) from Equations (5b)-(5d). The highlight response curve is of the form f(x)=1+k(x/S)^(n), where x is a color value ranging between 0 and S, and the highlight multiplier for pixel location (i,j) is obtained by applying the highlight response curve to I_(filter)(i,j), as in Equation (5b). Similarly, the shadow response curve is of the form f(x)=1+k(1−x/S)^(n), and the shadow multiplier for pixel location (i,j) is obtained by applying the shadow response curve to I_(filter)(i,j), as in Equation (5c). According to Equation (5d), the offset curve is given by S*[1−1/f(x)], where f(x) is the highlight response curve, and the offset value for pixel location (i,j) is obtained by applying the offset curve to I_(filter)(i,j). As the highlight response curve increases from 1 to 1+k, the offset curve increases from 0 to [k/(k+1)]*S.

Image enhancer 220 also includes an offset subtractor, 250, for subtracting offset values from source pixel color values, to obtain shifted color values; a highlight multiplier for multiplying the shifted color values by the highlight multipliers; and a shadow multiplier for further multiplying by the shadow multipliers. Together, offset subtractor 250, highlight multiplier 260 and shadow multiplier 270 carry out the operations in Equation (5a).

For purposes of clarification, the operation of image enhancer 220 has been described as it applied to grayscale images, using Equations (5a)-(5d). Similar, but more complex operations are performed when image enhancer 220 is applied to color images, in carrying out Equations (6a)-(6e).

Image enhancer 220 uses parameters k_(hl), k_(hc), k_(sl), k_(sc) and k_(CB), as described hereinabove, which are preferably adjustable by a user. Full details of operation of image enhancer 220, in accordance with a preferred embodiment of the present invention, are provided in the computer source code listing in Appendix B.

After the enhanced image is computed, a user interface manager 230 displays the enhanced image. A user viewing the image can interactively modify values of parameters, and in turn a corresponding modified enhanced image is derived by filter processor 210 and image enhancer 220.

Reference is now made to FIG. 3, which is a simplified flowchart of the essential steps for interactively enhancing digital images, in accordance with a preferred embodiment of the present invention. At step 310 a determination is made as to whether or not a user has adjusted one or more parameter values. If not, then processing waits at step 320 and periodically repeats the determination of step 310. There are two types of parameters; namely, filter-based parameters M, N and METHOD, and enhancement-based parameters k_(hl), k_(hc), k_(sl), k_(sc) and k_(CB), as described hereinabove.

If step 310 determines that one or more parameter values have been adjusted, then at step 330 a further determination is made as to whether or not any of the filter parameters have been adjusted. If so, then at step 340 a user interface sets values of the adjusted filter-based parameters, and at step 350 one or more filtered images are computed. Full details of performance of step 350, in accordance with a preferred embodiment of the present invention, are provided in the computer source code listing in Appendix A.

If step 330 determines that none of the filter parameters have been adjusted, then processing jumps to step 360, where a user interface sets values of the adjusted enhancement-based parameters. At step 370 an enhanced image is computed.

Step 370 preferably includes steps 371-376, which derive offset values and highlight and shadow multipliers, based on I_(filter), as per Equations (5b)-(5d); and apply them to the source image I_(source), as per Equation (5a). As mentioned hereinabove with respect to image enhancer 220 (FIG. 2), the steps shown in FIG. 3 correspond to grayscale image processing. Similar but more complex operations are performed when using Equations (6a)-(6e) for color images. Full details of performance of step 370, in accordance with a preferred embodiment of the present invention, are provided in the computer source code listing in Appendix B.

At step 380 the enhanced image is displayed within a user interface, and a user can interactively adjust parameter values, based on the appearance of the enhanced image. Processing returns to step 310, where the system checks if the user has adjusted parameter values.

Discussion of Experimental Results

Reference is now made to FIGS. 4A-4F, which illustrate an image before and after enhancement, an image of overexposed pixel locations for the enhanced image, and various filtered images used in the enhancement process, in accordance with a preferred embodiment of the present invention. Shown in FIG. 4A is a user interface display, which is used to view results of image enhancement, and interactively modify enhancement parameters. An original digital image 405, I_(source), is displayed. The original image suffers from poor illumination.

In a right panel of FIG. 4A are displayed various controls that are used to adjust parameters values. Controls 410, 415 and 420 are used to adjust shadow-related parameters. Control 410 is a slider bar for setting a value for the parameter k_(sl), through an intermediate value referred to as “Amount.” The value of the “Amount” parameter ranges from 0 to 100, and the value of k_(sl), ranges correspondingly from 0 to its maximum value, as described hereinbelow. The correspondence between k_(sl) and the “Amount” parameter may be linear or non-linear, increasing or decreasing. Control 420 is a slider bar for setting a value for the parameter k_(sc), referred to as “Range.” The value of the “Range” parameter ranges from 0 to 100, and the value of k_(sc) ranges correspondingly from 1 to 25. The correspondence between k_(sc) and the “Range” parameter may be linear or non-linear, increasing or decreasing. Control 430 is a slider bar, ranging from 0 to 100, for setting the window size, N, for the filter computation, as described hereinabove.

Controls 425, 430 and 435 are similar to controls 410, 415 and 420, respectively, but are used to adjust highlight-related parameters instead of shadow-related parameters.

Control 440 is used to set the color-boost parameter k_(CB). Control 445, “Color Priority,” is used to select either the embodiment described in Section 1.1.2 above, or the embodiment described in Section 1.1.3 above, for performing image enhancement for a color image; i.e., to select between using a filtered luminance image and using filtered minimum and maximum images, as described hereinabove. Control 50 is used to select the type of filter, from among the list:

Median—in accordance with Section 1.1 above;

Sigma—in accordance with method 1 for assigning weights in Section 1.2 above;

Gradient Inverse—in accordance with method 2 for assigning weights in Section 1.2 above;

SMCM—in accordance with method 3 for assigning weights in Section 1.2 above; and

SUSAN—in accordance with method 4 for assigning weights in Section 1.2 above

As shown in FIG. 4A, the default values of the parameter settings are: Amount=50, Range=60 and Detail=100 for the shadow-related parameters; Amount=0, Range=70 and Detail=100 for the highlight-related parameters; Color Boost=70 and Color Priority is set, indicating the embodiment that uses filtered minimum and maximum images. The default choice of filter is “SMCM.” It can be seen from Equations (6d) and (6e) that when the amount parameters are set to zero, so that k_(sl)=k_(hl)=0, then the value of the range parameters have no effect on the enhancement.

Preferably, a user can adjust controls 410-450 and view the corresponding enhanced image interactively. Shown in FIG. 4B is an enhanced image 455, I_(enhanced), having much better illumination and showing details that were not visible in the original image 405, I_(source). As seen in the figure, the user-adjusted values of the parameter settings are: Amount=100, Range=27 and Detail=100 for the shadow-related parameters; Amount=100, Range=40 and Detail=100 for the highlight-related parameters; Color Boost=70 and Color Priority is set, indicating the embodiment that uses filtered minimum and maximum images. The choice of filter is “Gradient Inverse.”

Also shown in FIG. 4B is a response curve 460, displaying the product f(n)=[n−g_(offset)(n)]*g_(min)(n)*g_(max)(n), as a function of color values, n, between 0 and S. The response curve is useful for visualizing the effect of the local contract enhancement on the shadows (low values of n) and on the highlights (high values of n). The highlight factor, g_(min)(n), increases from 1 to 1+k_(hl) as n ranges from 0 to S, and the shadows factor, g_(max)(n), decreases from 1+k_(sl) to 1, respectively, and the shapes of these curves changes from linear, when the exponents k_(hc) and k_(sc) are zero, to sub-linear when these exponents are positive. As such, the response curve 460 exhibits a shape like that of a cubic function of n. As can be seen from FIG. 4B, the response curve crosses the 45° line, where f(n)=n, three times, including n=0 and n=S and an intermediate value of n.

A control 465, “Exposure Warning,” is used to display pixel locations for which pixel color values as determined by Equations (6a)-(6d) were out of range, and had to be clipped to 0 or to S. Shown in FIG. 4C is a visualization of the out of range pixel locations.

FIGS. 4D and 4E show the filtered minimum and maximum images, MIN_(filter) and MAX_(filter), as described in Section 1.1.3 hereinabove, used in deriving the enhanced image shown in FIG. 4B. FIG. 4F shows the luminance image L_(filter), which is used when following the embodiment described in Section 1.1.2 hereinabove. It is noted that control 445 is checked in FIGS. 4D and 4E, indicating use of the Section 1.1.3 embodiment, and that control 445 is un-checked in FIG. 4F, indicating use of the Section 1.1.2 embodiment.

Studies of results have shown that the present invention suppresses details well in a source image, when such details are smaller than the radius of the filter window. The advantage of suppression of details derives from the use of filtered images to drive enhancement of the source image. The presence of detail in the filtered images suppresses the corresponding detail in the enhancement process, since dark areas are made relatively lighter and light areas are made relatively darker, where “lightness” and “darkness” of an area are determined by the filtered images. In enhancing the image, the present invention stretches contrast of small details, without stretching contrast of large details. This controlled stretching occurs when the filtered images (i) suppress smaller, high-contrast details, by amplitude reduction or blurring; and (ii) leave larger, high-contrast details intact, neither reducing them in amplitude nor blurring them. Blurring of large details in filtered images causes visible “halo” effects in the output image, which is a significant drawback in prior art systems, such as Adobe Photoshop®.

When larger details are present in the source image, the filter may exhibit insensitivity to small detail in transition zones between large bright areas and large dark areas, and such detail passes through the filter without much attenuation. Specifically, the transition zone is an area with width equal to the radius of the filter window and oriented along boundary contours between the dark and light areas.

Through empirical testing it was found that iterative application of the median filter with successively larger values of a filter radius, r₀, r₁, r₂, . . . , serves to resolve the above insensitivity. It was also found that, for large values of window radius, starting with an initial radius r₀ less than 10 gives negligible improvement. Thus, in practice, the sequence of radii used is N, N/2, N/4, . . . , terminating when a radius, N/2^(k+1), less than 10 is reached. Iterative application of the median filter then proceeds from the smallest radius, N/2^(k), to the largest radius, N. For example, when the median filter radius is 128, the successive values used for r₀, r₁, r₂, . . . are 16, 32, 64 and 128.

Similarly, for the weighted average filters, the value of N is increased by a factor of five at each iteration, starting with a minimum value of N=2.

Through empirical testing it was also found that for a given value of N, an optimal value for M is N/2. The parameter N may be user-specified. A default value of N is set to min(width, height)/2, where width and height are the pixel dimensions of the source image, and a user can adjust the value of N to between 0% and 100% of this default value. As such, the filter window can be as large as the entire image. It is noted that this choice for the maximum value of N makes the relative setting of this parameter practically resolution-independent. Thus, the optimal relative setting for N is practically the same, say, for a one mega-pixel image as for a six mega-pixel image.

It has further been found through experimentation, that the enhancement algorithm of the present invention is essentially invariant to scale. Specifically, enhancing, say, a six mega-pixel image and then sub-sampling to a one mega-pixel image produces an image that is nearly identical as the image produces by first sub-sampling and then enhancing. Such invariance to scale is an important advantage of the present invention, since enhancement can be performed on a sub-sampled image used for previewing while a user is adjusting enhancement parameters. When the user then commits the parameters, for example, by clicking on an “Apply” button, the full-resolution image can be enhanced, and the results enhanced image will appear as the user expects.

Through empirical testing, it has been found that the setting b=2 and n=2 in the sub-window configurations for the weighted average filter, represents a good trade-off between computational complexity and accuracy when enhancing typical digital photo resolutions on a typical personal computer.

Regarding choices for the parameters k_(hl), k_(hc), k_(sl) and k_(sc), results have shown that a good approach is to let the maximum value of k_(hl) depend on the value of k_(hc), and similarly to let the maximum value of k_(sl) depend on the value of k_(sc). In accordance with a preferred embodiment of the present invention, such maximum values are determined as follows:

-   -   1. Determine the largest highlights multiplier for a given value         of k_(hc); i.e., the largest value which, when multiplied by any         color value, I, gives a product I*g_(min)(I) no smaller than 0.         It can be seen from Equation (5b) that such value corresponds to         the minimum of

${\left( \frac{I}{256 - I} \right)/\left( \frac{I}{S} \right)^{k_{sc}}},$

-   -    over the range of values for I between 1 and 255. Similarly,         determine the largest shadow multiplier for a given value of         k_(sc); i.e., the largest value which, when multiplied by any         color value, I, gives a product I*g_(max)(I) no larger than 255.         It can be seen from Equation (5c) that such value corresponds to         the minimum of

${\left( {\frac{256}{I} - 1} \right)/\left( {1 - \frac{I}{S}} \right)^{k_{sc}}},$

-   -    over the range of values for 1 between 1 and 255.     -   2. For shadows, use min(256, (I+30)*2.5)) as the maximum value,         instead of 255. This adjustment serves to limit the maximum         multiplier when the value of k_(sc) is low. For example,         multiplying a shadow area by a factor of 10 raises the average         luminance of such area to 200, which is still within the dynamic         range, but yields poor results due to posterization and noise.     -   3. For shadows case, levels above 200 are not checked. This         adjustment serves to allow a certain amount of clipping when         k_(sc) is high. Otherwise, the maximum value of k_(sl) may be         set so low as to eliminate the effect of contrast enhancement.         For example, if k_(sc) is set to 100, then the response curve is         flat, and all areas of the image are multiplied by the same         multiplier. The subject adjustment enables a user to modify an         image to bring out some amount of clipping. Preferably, an         analogous adjustment is made for highlights for levels below 50.     -   4. The user-specified parameter within the graphical user         interface is a percentage between 0 and 100, which is multiplied         by the largest values of k_(hl) and k_(sl) as determined in         steps 1-3 above.         The listing in Appendix B provides sample source code for         determination of parameters k_(hl) and k_(sl) in accordance with         steps 1-4 above.

Regarding the parameters k1 and k2 used in determining the weights for the Gradient Inverse filter, as described with respect to Method 2 in Section 1.2 above, it has been found that a choice of k1=120 and k2=6 produces good results. Increasing k2 causes the filter to better preserve extreme local values. In general, a higher exponential rolloff rate tends to better preserve local extremes while simultaneously removing low-amplitude texture.

Discussion of Appendices

Reference is now made to FIG. 5, which is a flow diagram of the principal software methods in the source code listed in Appendices A and B, in accordance with a preferred embodiment of the present invention. The listing in Appendices A and B includes line numbers within methods, for ease of reference.

The main method Run( ), listed in Appendix B, calls ApplyFilter( ) at lines 22-26, which performs local contrast enhancement in accordance with a preferred embodiment of the present invention, and returns a bitmap pDIBDst.

The method ApplyFilter( ), listed in Appendix B, carries out Equations (6a)-(6e) hereinabove. At lines 24 and 25, ApplyFilter calls CreateMinMaxDIBs( ) to generate the filtered image MIN_(filter) and MAX_(filter), which are arrays accessed by member pointers m_pDIBMin and m_pDIBMax, respectively. The highlight and shadow multipliers g_(min) and g_(max) are tabulated and stored in look-up tables dFacLUTH[ ] and dfacLUTS[ ], respectively. The color boost parameter, k_(CB), is stored in the variable dCBColor. Lines 141-171 correspond to Equations (6a)-(6c) for 24-bit color images. Other code sections in ApplyFilter( ) correspond to 8-bit, 16-bit and 64-bit color images.

The method CreateMinMaxDIBs( ), listed in Appendix B, calls IP_LocalMinMax( ) at lines 65-67, which is the main method used to generate the filtered images MIN_(filter) and MAX_(filter), in accordance with a preferred embodiment of the present invention. CreateMinMaxDIBs( ) also generates the source luminance image, L_(source), by calling GetPixelVal( ) at line 27 and at line 47. L_(source) is an array accessed by a member pointer m_pDIBY.

The method IP_LocalMinMax( ), listed in Appendix A, generates the filtered images MIN_(filter) and MAX_(filter), and stored the results in arrays accessed by pointers pBitsMin and pBitsMax, respectively. The parameter pColorPriority determines whether to filter the luminance source image, as described in Sec. 1.1.2 hereinabove, or else to filter the maximum and minimum sources images, as described in Sec. 1.1.3 hereinabove. Correspondingly, at lines 50 and 51 GetMinMaxImages( ) is called, and at lines 59 and 60 GetLuminanceImage( ) is called. The parameter iMethod determines whether to use a median filter, as described in Sec. 1.1 hereinabove, or a weighted average filter, as described in Sec. 1.2 hereinabove. Correspondingly, at lines 89 and 90 the method IP_HybridMedianFilter( ) is called, and at lines 92 and 93 the method IP_HybridWeightedAverageFilter( ) is called, for computing MIN_(filter). Similarly, at lines 125 and 126 the method IP_HybridMedianFilter( ) is called, and at lines 128 and 129 the method IP_HybridWeightedAverageFilter( ) is called, for computing MAX_(filter).

The method IP_HybridMedianFilter( ), listed in Appendix A, carries out Equations (2a)-(2g) hereinabove. At lines 52 and 53, the method ComputeHorizontalAverages( ) is called, to compute various sub-window averages, as described hereinbelow, and at lines 84-91 the appropriate averages are stored in arrays pAveWindowWest, pAveWindowEast, etc. The Equations (3a)-(3c) are carried out at lines 101-121 using the method opt_med3( ) to compute the median of three numbers.

The method IP_HybridWeightedAverageFilter( ), listed in Appendix A, carries out Equation (4) hereinabove. The parameter iMethod is used to determine the weights that are used, in accordance with methods 1-4 described hereinabove in Sec. 1.2, as can be seen in lines 53-79. The weights are tabulated in an array pnWeights[ ]. For method 4, the weights are modified at lines 99 and 100, to incorporate the multiplication by exp(−r²/k²). At lines 110 and 111 the method ComputeHorizonalAverages( ) is called, to compute various sub-window averages, as described hereinbelow. The weighted average in Equation (4) is computed at lines 159-166 and lines 183-190.

The method GetMinMaxImages( ), listed in Appendix A, computes the source minimum and maximum images, MIN_(source) and MAX_(source), using the methods GetPixelMin( ) and GetPixelMax( ), respectively. Similarly, the method GetLuminanceImage( ), listed in Appendix B, computes the luminance source image, L_(source), using the method GetPixelVal( ).

The method ComputeHorizontalAverages( ), listed in Appendix A, computers one-dimensional horizontal (2M+1)×1 sub-window averages. These horizontal averages are then averaged vertically at lines 61-76 of method IP_HybridMedianFilter( ) and lines 119-134 of method IP_HybridWeightedAverageFilter( ), to derive the two-dimensional (2M+1)×(2M+1) sub-window averages.

The method CreateResponseCurves( ), listed in Appendix B, computers the response curves corresponding to g_(min) and g_(max). Lines 6-22 correspond to determination of maximum values for k_(hl) and k_(sl), based upon values of k_(hc) and k_(sc), respectively, as described hereinabove.

In the foregoing specification, the invention has been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made to the specific exemplary embodiments without departing from the broader spirit and scope of the invention as set forth in the appended claims. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. 

1. A computer processor-based method for contrast enhancement for digital images, comprising: filtering, by a computer processor controlled electronic device, an original image, denoted ORIG, having original color values ORIG(i,j), to generate a first filtered image, denoted F1, and a second filtered image, denoted F2, wherein the first and second filtered images have smooth local color variation relative to the original image; deriving, by the electronic device, local highlight scale factors, denoted LH(i,j), by applying a variable highlight response curve, denoted HRC, to the first filtered image, LH(i,j)=HRC(F1(i,j)); deriving, by the electronic device, local shadow scale factors, denoted LS(i,j), by applying a variable shadow response curve, denoted SRC, to the second filtered image, LS(i,j)=SRC(F2(i,j)); deriving, by the electronic device, local offset values, denoted OFST(i,j), by applying an offset curve, denoted OC, to the first filtered image, OFST(i,j)=OC(F1(i,j)); and processing, by the electronic device, the original image ORIG, comprising: subtracting the local offset values from the original color values to generate shifted color values; scaling the shifted color values by the local highlight scale factors; and further scaling the shifted color values by the local shadow scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LS*LH*(ORIG−OFST).
 2. The method of claim 1 wherein the first and second filtered images F1 and F2 are the same image.
 3. The method of claim 2 wherein the first and second filtered images F1 and F2 are obtained from luminance color values of the original image ORIG.
 4. The method of claim 1 wherein the first and second filtered images F1 and F2 are different images.
 5. The method of claim 4 wherein the first filtered image F1 is obtained from minimum red-green-blue color values of the original image ORIG, and wherein the second filtered image F2 is obtained from maximum red-green-blue color values of the original image ORIG.
 6. The method of claim 1 wherein the first and second filtered images F1 and F2 are computed using medians.
 7. The method of claim 6 wherein the medians are medians of sub-averages.
 8. The method of claim 1 wherein the first and second filtered images F1 and F2 are computed using weighted averages.
 9. The method of claim 8 wherein the weighted averages are weighted averages of sub-averages.
 10. The method of claim 8 wherein the weighted averages use weight coefficients that have an inverse gradient dependency on input values.
 11. The method of claim 8 wherein the weighted averages use weight coefficients that have an inverse distance dependency on input positions.
 12. The method of claim 1 wherein said filtering is based on values of at least one parameter, and further comprising setting at least one such parameter value by a user.
 13. The method of claim 12 further comprising rendering, by the electronic device, the contrast-enhanced image ENHNCD on a display device, and wherein said setting at least one such parameter value is performed in response to said rendering.
 14. The method of claim 13 wherein said processing, said rendering and said setting at least one such parameter value are performed repeatedly until a satisfactory contrast-enhanced image ENHNCD is obtained.
 15. The method of claim 1 wherein said deriving local highlight scale factors LH comprises receiving at least one parameter value set by a user for determining the highlight response curve HRC, and wherein said deriving local shadow scale factors LS comprises receiving at least one parameter value set by a user for determining the shadow response curve SRC.
 16. The method of claim 15 further comprising rendering, by the electronic device, the contrast-enhanced image ENHNCD on a display device, and wherein said setting at least one such parameter value is performed in response to said rendering.
 17. The method of claim 16 wherein said processing, said rendering and said setting at least one such parameter value are performed repeatedly until a satisfactory contrast-enhanced image ENHNCD is obtained.
 18. A non-transitory computer readable storage medium containing instructions which when executed implement a system for enhancing contrast of digital images, comprising: instructions for a filter processor for filtering an original image, denoted ORIG, having original color values ORIG(i,j), to generate a first filtered image, denoted F1, and a second filtered image, wherein the first and second filtered images have smooth local color variation relative to the original image; and instructions for an image enhancer coupled to said filter processor for (i) deriving local highlight linear scale factors, denoted LH(i,j), by applying a variable highlight response curve, denoted HRC, to the first filtered image, LH(i,j)=HRC(F1(i,j)), (ii) deriving local shadow linear scale factors, denoted LS(i,j), by applying a variable shadow response curve, denoted SRC, to the second filtered image, LS(i,j)=SRC(F2(i,j)), (iii) deriving local offset values, denoted OFST(i,j), by applying an offset curve, denoted OC, to the first filtered image, OFST(i,j)=OC(F1(i,j)), (iv) subtracting the local offset values from the original color values to generate shifted color values, (v) scaling the shifted color values by the local highlight scale factors, and (vi) further scaling the shifted color values by the local shadow scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LS*LH*(ORIG−OFST).
 19. The medium of claim 18 wherein the first and second filtered images F1 and F2 are the same image.
 20. The medium of claim 19 wherein the first and second filtered images F1 and F2 are obtained from luminance color values of the original image ORIG.
 21. The medium of claim 18 wherein the first and second filtered images F1 and F2 are different images.
 22. The medium of claim 21 wherein the first filtered image F1 is obtained from minimum red-green-blue color values of the original image ORIG, and wherein the second filtered image F2 is obtained from maximum red-green-blue color values of the original image ORIG.
 23. The medium of claim 18 wherein said filter processor uses medians.
 24. The medium of claim 23 wherein the medians are medians of sub-averages.
 25. The medium of claim 18 wherein said filter processor uses weighted averages.
 26. The medium of claim 25 wherein the weighted averages are weighted averages of sub-averages.
 27. The medium of claim 25 wherein the weighted averages use weight coefficients that have an inverse gradient dependency on input values.
 28. The medium of claim 25 wherein the weighted averages use weight coefficients that have an inverse distance dependency on input positions.
 29. The medium of claim 18 wherein said filter processor uses values of at least one parameter, and further comprising instructions for a user interface for setting at least one such parameter value by a user.
 30. The medium of claim 29 wherein said user interface renders the contrast-enhanced image ENHNCD on a display device, and wherein the user sets at least one such parameter value in response to viewing the contrast-enhanced image ENHNCD on the display device.
 31. The medium of claim 18 wherein said image enhancer uses values of at least one parameter, and further comprising instructions for a user interface for setting at least one such parameter value by a user.
 32. The medium of claim 31 wherein said user interface renders the contrast-enhanced image ENHNCD on a display device, and wherein the user sets at least one such parameter value in response to viewing the contrast-enhanced image ENHNCD on the display device.
 33. A computer processor-based method for contrast enhancement for digital images, comprising: filtering, by a computer processor controlled electronic device, an original image, denoted ORIG, having original color values ORIG(i,j), to generate a filtered image, denoted F, wherein the filtered image has smooth local color variation relative to the original image; deriving, by the electronic device, local highlight linear scale factors, denoted LH(i,j), by applying a variable highlight response curve, denoted HRC, to the filtered image, LH(i,j)=HRC(F(i,j)); deriving, by the electronic device, local offset values, denoted OFST(i,j), by applying an offset curve, denoted OC, to the filtered image, OFST(I,j)=OC(F(i,j)); and processing, by the electronic device, the original image ORIG, comprising: subtracting the local offset values from the original color values to generate shifted color values; and scaling the shifted color values by the local highlight scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LH*(ORIG−OFST).
 34. A computer processor-based method for contrast enhancement for digital images, comprising: filtering, by a computer processor controller electronic device, an original image, denoted ORIG, having original color values ORIG(i,j), to generate a filtered image, denoted F, wherein the filtered image has smooth local color variation relative to the original image; deriving, by the electronic device, local shadow linear scale factors, denoted LS(i,j), by applying a variable shadow response curve, denoted SRC, to the filtered image, LS(i,j)=SRC(F(i,j)); and processing, by the electronic device, the original image ORIG, comprising scaling the original color values by the local shadow scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LS*ORIG.
 35. A non-transitory computer readable storage medium containing instructions which when executed implement a system for enhancing contrast of digital images, comprising: instructions for a filter processor for filtering an original image, denoted ORIG, having original color values ORIG(i,j), to generate a filtered image, denoted F, wherein the filtered image has smooth local color variation relative to the original image; and instructions for an image enhancer coupled to said filter processor for (i) deriving local highlight local scale factors, denoted LH(i,j), by applying a variable highlight response curve, denoted HRC, to the filtered image, LH(i,j)=HRC(F(i,j)), (ii) deriving local offset values, denoted OFST(i,j), by applying an offset curve, denoted OC, to the filtered image, OFST(i,j)=OC(F(i,j)), (iii) subtracting the local offset values from the original color values to generate shifted color values, and (iv) scaling the shifted color values by the local highlight scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LH*(ORIG−OFST).
 36. A non-transitory computer readable storage medium containing instructions which when executed implement a system for enhancing contrast of digital images, comprising: instructions for a filter processor for filtering an original image, denoted ORIG, having original color values ORIG(i,j), to generate a filtered image, denoted F, wherein the filtered image has smooth local color variation relative to the original image; and instructions for an image enhancer coupled to said filter processor for (i) deriving local shadow linear scale factors, denoted LS(i,j), by applying a variable shadow response curve, denoted SRC, to the filtered image, LS(i,j)=SRC(F(i,j)), and (ii) scaling the original color values by the local shadow scale factors, thereby generating a contrast-enhanced image from the original image, ENHNCD=LS*ORIG.
 37. The method of claim 1 wherein the highlight response curve HRC is a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, and the shadow response curve SRC is a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value.
 38. The method of claim 15 wherein the highlight response curve is determined by HRC(x)=1+a*(x/S)^(b), for non-zero parameters a and b that may be set by a user, where S denotes the largest possible value for x, wherein the shadow response curve is determined by SRC(x)=1+c*(1−x/S)^(d), for non-zero parameters c and d that may be set by a user, and wherein the offset curve is determined by OC(x)=S*[1−1/HRC(x)].
 39. The medium of claim 18 wherein the highlight response curve HRC is a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value, and the shadow response curve SRC is a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value.
 40. The medium of claim 31 wherein the highlight response curve is determined by HRC(x)=1+a*(x/S)^(b), for non-zero parameters a and b that may be set by a user, where S denotes the largest possible value for x, wherein the shadow response curve is determined by SRC(x)=1+c*(1−x/S)^(d), for non-zero parameters c and d that may be set by a user, and wherein the offset curve is determined by OC(x)=S*[1−1/HRC(x)].
 41. The method of claim 33 wherein the highlight response curve HRC is a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value.
 42. The method of claim 34 wherein the shadow response curve SRC is a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value.
 43. The medium of claim 35 wherein the highlight response curve HRC is a function of color value that increases from a response value of one, corresponding to a color value of zero, to a response value greater than one, corresponding to a maximum color value.
 44. The medium of claim 36 wherein the shadow response curve SRC is a function of color value that decreases from a response value greater than one, corresponding to a color value of zero, to a response value of one, corresponding to a maximum color value. 